prep

load packages

library(tidyverse)
library(lme4)
library(lmerTest)
library(knitr)

define aesthetics

algorithm = c("#006989", "#FEC601", "#F43C13", "#00A5CF", "#00A878")
food = c("#A4C960", "#2A5C8C")
liking = c("#FF0000", "#F2AD00")
bid = c("#00A08A", "#F2AD00", "#F98400", "#FF0000")
dc_bw = plot_aes = theme_minimal() +
  theme(legend.position = "top",
        legend.text = element_text(size = 12),
        text = element_text(size = 16, family = "Futura Medium"),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        axis.text = element_text(color = "black"),
        axis.line = element_line(colour = "black"),
        axis.ticks.y = element_blank())

define functions

table_model = function(model_data) {
  model_data %>%
    broom.mixed::tidy(., conf.int = TRUE) %>%
    rename("SE" = std.error,
           "t" = statistic,
           "p" = p.value) %>%
    filter(effect == "fixed") %>%
    select(-group, -effect) %>%
    mutate_at(vars(-contains("term"), -contains("value")), round, 2) %>%
    mutate(term = gsub(":", " x ", term),
           term = gsub("dot_", "", term),
           term = gsub("liking_rating", "liking", term),
           term = gsub("healthunhealthy", "health (unhealthy)", term),
           p = ifelse(p < .001, "< .001",
                      ifelse(p == 1, "1.000", gsub("0.(.*)", ".\\1", sprintf("%.3f", p)))),
           `b [95% CI]` = sprintf("%.2f [%0.2f, %.2f]", estimate, conf.low, conf.high)) %>%
    select(term, `b [95% CI]`, df, t, p) %>%
    kable()
}

load and tidy task data

task = read.csv("~/Documents/code/sanlab/DEV_scripts/fMRI/fx/multiconds/WTP/betaseries/events.csv", stringsAsFactors = FALSE) %>%
  mutate(bid = ifelse(bid == "NULL", NA, bid),
         bid = as.integer(bid),
         health = as.factor(health),
         liking = as.factor(liking)) %>%
  group_by(subjectID, wave, run) %>%
  mutate(trial = row_number()) %>%
  filter(wave %in% 1:2)

check responses

Check if wrong buttons were used (i.e., not 5-8)

DEV002 = wrong button box (i.e. 1-4) used
DEV007 = wrong button box (i.e. 1-4) used
DEV011 = code normally
DEV017 = exclude; can’t tell if they’re missed responses or incorrect placement of fingers
DEV019 = exclude; can’t tell if they’re missed responses or incorrect placement of fingers
DEV032 = code normally
DEV033 = incorrect placement of fingers; recode runs 1-3
DEV054 = exclude; technical error?
DEV061 = code normally

NB! NEED TO GO THROUGH NEW PARTICPANTS/WAVES

subs = task %>%
  mutate(bid = as.character(bid)) %>%
  group_by(subjectID, run, bid) %>%
  summarize(n = n()) %>%
  spread(bid, n) %>%
  mutate(messed = ifelse(!is.na(`2`), "yes", NA),
         messed = ifelse(is.na(`5`) & !is.na(`<NA>`), "yes", messed)) %>%
  filter(messed == "yes") %>% 
  ungroup() %>% 
  select(subjectID) %>% 
  unique()

task %>%
  mutate(bid = as.character(bid)) %>%
  group_by(subjectID, run, bid) %>%
  summarize(n = n()) %>%
  spread(bid, n) %>%
  mutate(messed = ifelse(!is.na(`2`), "yes", NA),
         messed = ifelse(is.na(`5`) & !is.na(`<NA>`), "yes", messed)) %>%
  filter(subjectID %in% subs$subjectID)

recode and exclude

Recoding
* DEV033: recode runs1-3, but if liking rating < 3, leave as missing

NB! NEED TO GO THROUGH NEW PARTICPANTS/WAVES

data.ex = task %>%
  mutate(bid = ifelse(subjectID == "DEV033" & wave == 1 & !run == "run4", bid - 1, bid),
         bid = ifelse(subjectID == "DEV033" & wave == 1 & !run == "run4" & is.na(bid) & liking_rating > 2, 8, bid),
         bid = (bid - 5) / 2) %>%
  group_by(subjectID, wave) %>%
  arrange(subjectID, run) %>%
  mutate(trial = row_number())

load mean intensity values

file_dir = "~/Documents/code/sanlab/DEV_scripts/fMRI/betaseries/WTP/dotProducts_WTP_wave1/"
file_pattern = "DEV[0-9]{3}_meanIntensity.txt"
file_list = list.files(file_dir, pattern = file_pattern)

intensities = data.frame()

for (file in file_list) {
  temp = tryCatch(read.table(file.path(file_dir,file), fill = TRUE) %>%
                    rename("subjectID" = V1,
                           "meanIntensity" = V3) %>%
                    extract(V2, "beta", "beta_([0-9]{4}).nii") %>%
                    mutate(beta = as.integer(beta),
                           wave = 1), error = function(e) message(file))
  intensities = rbind(intensities, temp)
  rm(temp)
}

file_dir = "~/Documents/code/sanlab/DEV_scripts/fMRI/betaseries/WTP/dotProducts_WTP_wave2/"
file_list = list.files(file_dir, pattern = file_pattern)
for (file in file_list) {
  temp = tryCatch(read.table(file.path(file_dir,file), fill = TRUE) %>%
                    rename("subjectID" = V1,
                           "meanIntensity" = V3) %>%
                    extract(V2, "beta", "beta_([0-9]{4}).nii") %>%
                    mutate(beta = as.integer(beta),
                           wave = 2), error = function(e) message(file))
  intensities = rbind(intensities, temp)
  rm(temp)
}

load dot products

file_dir = "~/Documents/code/sanlab/DEV_scripts/fMRI/betaseries/WTP/dotProducts_WTP_wave1/"
file_pattern = "DEV[0-9]{3}_dotProducts.txt"
file_list = list.files(file_dir, pattern = file_pattern)

dots = data.frame()

for (file in file_list) {
  temp = tryCatch(read.table(file.path(file_dir,file), fill = TRUE) %>%
                    rename("subjectID" = V1,
                           "map" = V3,
                           "dotProduct" = V4) %>%
                    extract(V2, "beta", "beta_([0-9]{4}).nii") %>%
                    extract(map, "algorithm", "(.*)_.*.nii") %>%
                    mutate(beta = as.integer(beta),
                            wave = 1), error = function(e) message(file))
  dots = rbind(dots, temp)
  rm(temp)
}

file_dir = "~/Documents/code/sanlab/DEV_scripts/fMRI/betaseries/WTP/dotProducts_WTP_wave2/"
file_list = list.files(file_dir, pattern = file_pattern)

for (file in file_list) {
  temp = tryCatch(read.table(file.path(file_dir,file), fill = TRUE) %>%
                    rename("subjectID" = V1,
                           "map" = V3,
                           "dotProduct" = V4) %>%
                    extract(V2, "beta", "beta_([0-9]{4}).nii") %>%
                    extract(map, "algorithm", "(.*)_.*.nii") %>%
                    mutate(beta = as.integer(beta),
                            wave = 2), error = function(e) message(file))
  dots = rbind(dots, temp)
  rm(temp)
}

join intensities and dots

recode trials with extreme intensities as NA

dots.merged = dots %>%
  left_join(., intensities, by = c("subjectID", "wave", "beta")) %>%
  group_by(subjectID, wave, algorithm) %>%
  mutate(rownum = row_number())

# plot original
dots.merged %>%
  filter(algorithm == "craving_regulation") %>%
  ggplot(aes(1, meanIntensity)) +
    geom_boxplot()

# assess extreme values and exclude when calculating SDs
dots.merged %>%
  filter(algorithm == "craving_regulation") %>%
  arrange(meanIntensity)

dots.merged %>%
  filter(algorithm == "craving_regulation") %>%
  arrange(-meanIntensity)

# recode outliers as NA
dots.merged = dots.merged %>%
  ungroup() %>%
  mutate(meanIntensity = ifelse(meanIntensity > 1 | meanIntensity < -1, NA, meanIntensity),
         median = median(meanIntensity, na.rm = TRUE),
         sd3 = 3*sd(meanIntensity, na.rm = TRUE),
         outlier = ifelse(meanIntensity > median + sd3 | meanIntensity < median - sd3, "yes", "no"),
         dotProduct = ifelse(outlier == "yes", NA, dotProduct))
  
# plot after
dots.merged %>%
  filter(algorithm == "craving_regulation") %>%
  ggplot(aes(1, meanIntensity)) +
  geom_boxplot()

exclude outliers and standardize

standardize within algorithm and contrast

dots.ex = dots.merged %>%
  group_by(algorithm, wave, subjectID) %>%
  mutate(trial = row_number())

merge data

Exclusions

Didn’t scan: DEV002, DEV007
MRI motion and data quality exclusions: DEV001, DEV020, DEV032, DEV047, DEV063, DEV067, DEV078
Button box exclusions: DEV017, DEV019, DEV054
Run exclusions: DEV028 (run1), DEV048 (run3), DEV064 (run1), DEV069 (run1)

NB! NEED TO GO THROUGH NEW PARTICPANTS/WAVES

data_all = left_join(dots.ex, data.ex, by = c("subjectID", "wave", "trial")) %>%
  filter(!subjectID %in% c("DEV002", "DEV007", "DEV001", "DEV020", "DEV032", "DEV047", "DEV063", "DEV067", "DEV078", "DEV017", "DEV019", "DEV054")) %>%
  filter(!(subjectID == "DEV028" & run == "run1" & wave == 1) & !(subjectID == "DEV048" & run == "run3" & wave == 1) &
         !(subjectID == "DEV064" & run == "run1" & wave == 1) & !(subjectID == "DEV069" & run == "run1" & wave == 1)) %>%
  ungroup() %>%
  ungroup() %>%
  mutate(algorithm = gsub("_signature", "", algorithm),
         wave = as.character(wave),
         liking = ifelse(liking_rating > 2, "liked",
                  ifelse(liking_rating < 3, "disliked", NA))) %>%
  filter(!is.na(health))

data = data_all %>%
  filter(liking == "liked")

descriptives

number of participants

data %>%
  select(subjectID, wave) %>%
  unique() %>%
  group_by(wave) %>%
  summarize(n = n())

bids

across waves

data %>%
  group_by(health) %>%
  summarize(n = n(),
            mean = round(mean(bid, na.rm = TRUE), 2),
            sd = round(sd(bid, na.rm = TRUE), 2),
            min = min(bid, na.rm = TRUE),
            max = max(bid, na.rm = TRUE)) %>%
  kable(format = "pandoc")

health	n	mean	sd	min	max
healthy	24744	1.03	0.42	0	1.5
unhealthy	19674	0.91	0.48	0	1.5

by wave

data %>%
  group_by(wave, health) %>%
  summarize(n = n(),
            mean = round(mean(bid, na.rm = TRUE), 2),
            sd = round(sd(bid, na.rm = TRUE), 2),
            min = min(bid, na.rm = TRUE),
            max = max(bid, na.rm = TRUE)) %>%
  kable(format = "pandoc")

wave	health	n	mean	sd	max
1	healthy	13074	1.02	0.42	1.5
1	unhealthy	10341	1.01	0.43	1.5
2	healthy	11670	1.04	0.43	1.5
2	unhealthy	9333	0.81	0.50	1.5

session 0 liking ratings

data_all %>%
  group_by(health) %>%
  summarize(n = n(),
            mean = round(mean(liking_rating, na.rm = TRUE), 2),
            sd = round(sd(liking_rating, na.rm = TRUE), 2),
            min = min(liking_rating, na.rm = TRUE),
            max = max(liking_rating, na.rm = TRUE)) %>%
  kable(format = "pandoc")

health	n	mean	sd	min	max
healthy	46785	2.58	1.18	0	4
unhealthy	46794	2.22	1.23	0	4

correlations among signatures

craving & Koban craving

data %>%
  select(subjectID, wave, trial, algorithm, dotProduct) %>%
  spread(algorithm, dotProduct) %>%
  mutate(sub_wave = sprintf("%s_%s", subjectID, wave)) %>%
  rmcorr::rmcorr(as.factor(sub_wave), craving, nsc_koban, data = .)

## 
## Repeated measures correlation
## 
## r
## -0.08138986
## 
## degrees of freedom
## 14112
## 
## p-value
## 0.0000000000000000000003508496
## 
## 95% confidence interval
## -0.0977577 -0.06497801

craving & craving regulation

data %>%
  select(subjectID, wave, trial, algorithm, dotProduct) %>%
  spread(algorithm, dotProduct) %>%
  mutate(sub_wave = sprintf("%s_%s", subjectID, wave)) %>%
  rmcorr::rmcorr(as.factor(sub_wave), craving, craving_regulation, data = .)

## 
## Repeated measures correlation
## 
## r
## 0.1379588
## 
## degrees of freedom
## 14112
## 
## p-value
## 0.0000000000000000000000000000000000000000000000000000000000006315758
## 
## 95% confidence interval
## 0.1217368 0.1541071

Koban craving & craving regulation

data %>%
  select(subjectID, wave, trial, algorithm, dotProduct) %>%
  spread(algorithm, dotProduct) %>%
  mutate(sub_wave = sprintf("%s_%s", subjectID, wave)) %>%
  rmcorr::rmcorr(as.factor(sub_wave), nsc_koban, craving_regulation, data = .)

## 
## Repeated measures correlation
## 
## r
## -0.06609946
## 
## degrees of freedom
## 14112
## 
## p-value
## 0.000000000000003820737
## 
## 95% confidence interval
## -0.08250859 -0.0496545

visualize raw data

Only use liked foods (i.e., foods with a liking rating at session 0 of >2) for analyses other than when looking at relationships with liking specifically

bids only

across waves

data %>% 
  filter(algorithm == "craving") %>%
  ggplot(aes(health, bid, fill = health)) +
  stat_summary(fun = mean, geom = "bar", position = position_dodge(.5)) +
  stat_summary(fun.data = mean_cl_boot, geom = "errorbar", position = position_dodge(.5), width = 0) +
  scale_fill_manual(values = food) +
  labs(y = "bid value ($)\n", x = "\nfood type") +
  dc_bw +
  theme(legend.position = "none")

by wave

data %>% 
  filter(algorithm == "craving") %>%
  ggplot(aes(health, bid, fill = wave)) +
  stat_summary(fun = mean, geom = "bar", position = position_dodge(.95)) +
  stat_summary(fun.data = mean_cl_boot, geom = "errorbar", position = position_dodge(.95), width = 0) +
  scale_fill_manual(values = algorithm) +
  labs(y = "bid value ($)\n", x = "\nfood type") +
  dc_bw

bar plots

health

across waves

data %>% 
  ggplot(aes(health, dotProduct, fill = health)) +
  stat_summary(fun = mean, geom = "bar", position = position_dodge(.5)) +
  stat_summary(fun.data = mean_cl_boot, geom = "errorbar", position = position_dodge(.5), width = 0) +
  scale_fill_manual(values = food) +
  facet_grid(~algorithm) +
  labs(y = "signature expression\n", x = "\nfood type") +
  dc_bw +
  theme(legend.position = "none")

by wave

data %>% 
  ggplot(aes(health, dotProduct, fill = wave)) +
  stat_summary(fun = mean, geom = "bar", position = position_dodge(.95)) +
  stat_summary(fun.data = mean_cl_boot, geom = "errorbar", position = position_dodge(.95), width = 0) +
  scale_fill_manual(values = algorithm) +
  facet_grid(~algorithm) +
  labs(y = "signature expression\n", x = "\nfood type") +
  dc_bw

bids

across waves

data %>% 
  ggplot(aes(bid, dotProduct)) +
  stat_summary(fun = mean, geom = "bar", position = position_dodge(.5)) +
  stat_summary(fun.data = mean_cl_boot, geom = "errorbar", position = position_dodge(.5), width = 0) +
  facet_grid(~algorithm) +
  labs(y = "signature expression\n", x = "\nbid value ($)") +
  dc_bw

by wave

data %>% 
  ggplot(aes(bid, dotProduct, fill = wave)) +
  stat_summary(fun = mean, geom = "bar", position = position_dodge(.5)) +
  stat_summary(fun.data = mean_cl_boot, geom = "errorbar", position = position_dodge(.5), width = 0) +
  facet_grid(~algorithm) +
  scale_fill_manual(values = algorithm) +
  labs(y = "signature expression\n", x = "\nbid value ($)") +
  dc_bw

bid x health

across waves

data %>% 
  ggplot(aes(bid, dotProduct, fill = health)) +
  stat_summary(fun = mean, geom = "bar", position = position_dodge(.5)) +
  stat_summary(fun.data = mean_cl_boot, geom = "errorbar", position = position_dodge(.5), width = 0) +
  scale_fill_manual(values = food) +
  facet_grid(~algorithm) +
  labs(y = "signature expression\n", x = "\nbid value ($)") +
  dc_bw

by wave

data %>% 
  ggplot(aes(bid, dotProduct, fill = wave)) +
  stat_summary(fun = mean, geom = "bar", position = position_dodge(.5)) +
  stat_summary(fun.data = mean_cl_boot, geom = "errorbar", position = position_dodge(.5), width = 0) +
  scale_fill_manual(values = algorithm) +
  facet_grid(health~algorithm) +
  labs(y = "signature expression\n", x = "\nbid value ($)") +
  dc_bw

scatterplots

bids

across waves

data %>% 
  ggplot(aes(dotProduct, bid, color = health, fill = health)) +
  geom_smooth(aes(group = interaction(subjectID, health)), method = "lm", se = FALSE, size = .1) +
  geom_smooth(method = "lm") +
  scale_color_manual(values = food) +
  scale_fill_manual(values = food) +
  facet_grid(~algorithm, scales = "free") +
  labs(x = "\nsignature expression", y = "bid value ($)\n") +
  dc_bw

by wave

data %>% 
  ggplot(aes(dotProduct, bid, color = wave, fill = wave)) +
  geom_smooth(aes(group = interaction(subjectID, wave)), method = "lm", se = FALSE, size = .1) +
  geom_smooth(method = "lm") +
  scale_color_manual(values = algorithm) +
  scale_fill_manual(values = algorithm) +
  facet_grid(health~algorithm, scales = "free") +
  labs(x = "\nsignature expression", y = "bid value ($)\n") +
  dc_bw

liking

data_all %>% 
  ggplot(aes(dotProduct, liking_rating, color = health, fill = health)) +
  geom_smooth(aes(group = interaction(subjectID, health)), method = "lm", se = FALSE, size = .1) +
  geom_smooth(method = "lm") +
  scale_color_manual(values = food) +
  scale_fill_manual(values = food) +
  facet_grid(~algorithm, scales = "free") +
  labs(x = "\nsignature expression", y = "liking rating\n") +
  dc_bw +
  theme(legend.position = "top")

MLM

Disaggregate within and between person relationships

dot_between = grand mean centered person average signature expression
dot_within = person-centered signature expression

between = data %>%
  group_by(wave, subjectID, algorithm) %>%
  summarize(dot_between = mean(dotProduct, na.rm = TRUE)) %>%
  ungroup() %>%
  mutate(dot_between = scale(dot_between, center = TRUE, scale = TRUE))

data_diss = data %>%
  group_by(wave, subjectID, algorithm) %>%
  mutate(dot_within = scale(dotProduct, center = TRUE, scale = TRUE)) %>%
  left_join(., between) %>%
  select(subjectID, wave, trial, health, bid, liking_rating, algorithm, dotProduct, dot_within, dot_between)

data_diss_craving = data_diss %>%
  filter(algorithm == "craving")

data_diss_craving_koban = data_diss %>%
  filter(algorithm == "nsc_koban")

data_diss_regulation = data_diss %>%
  filter(algorithm == "craving_regulation")

data_diss_liking = data_all %>%
  group_by(wave, subjectID, algorithm) %>%
  mutate(dot_within = scale(dotProduct, center = TRUE, scale = TRUE)) %>%
  left_join(., between) %>%
  select(subjectID, wave, trial, health, bid, liking_rating, algorithm, dotProduct, dot_within, dot_between)

data_diss_liking_craving = data_diss_liking %>%
  filter(algorithm == "craving")

data_diss_liking_craving_koban = data_diss_liking %>%
  filter(algorithm == "nsc_koban")

data_diss_liking_regulation = data_diss_liking %>%
  filter(algorithm == "craving_regulation")

no brain

bid ~ wave x health

the intervention decreased bids for unhealthy foods and slightly increased bids for healthy foods

tidytable

mod_bid_health = lmer(bid ~ health * wave + (1 | subjectID),
                  data = data_diss_craving,
                  control = lmerControl(optimizer = "bobyqa"))
table_model(mod_bid_health)

term	b [95% CI]	df	t	p
(Intercept)	1.01 [0.98, 1.03]	403.12	83.40	< .001
health (unhealthy)	-0.00 [-0.02, 0.02]	13693.92	-0.27	.790
wave2	0.03 [0.01, 0.05]	13783.30	2.92	< .001
health (unhealthy) x wave2	-0.22 [-0.25, -0.19]	13615.08	-15.60	< .001

plot

ggeffects::ggpredict(mod_bid_health, terms = c("wave", "health")) %>%
  data.frame() %>%
  ggplot(aes(x, predicted, color = group, fill = group)) +
  geom_pointrange(aes(ymin = conf.low, ymax = conf.high)) +
  geom_line(aes(group = group)) +
  scale_color_manual(name = "", values = food) + 
  scale_fill_manual(name = "", values = food) + 
  labs(x = "\nwave", y = "predicted bid value\n") + 
  dc_bw

model summary

summary(mod_bid_health)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: bid ~ health * wave + (1 | subjectID)
##    Data: data_diss_craving
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 15471.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2282 -0.6856  0.0327  0.8142  2.5547 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  subjectID (Intercept) 0.02684  0.1638  
##  Residual              0.17135  0.4139  
## Number of obs: 13852, groups:  subjectID, 263
## 
## Fixed effects:
##                           Estimate   Std. Error           df t value
## (Intercept)               1.007886     0.012085   403.124914  83.402
## healthunhealthy          -0.002687     0.009959 13693.919404  -0.270
## wave2                     0.028070     0.009620 13783.295490   2.918
## healthunhealthy:wave2    -0.221797     0.014219 13615.082544 -15.599
##                                   Pr(>|t|)    
## (Intercept)           < 0.0000000000000002 ***
## healthunhealthy                    0.78735    
## wave2                              0.00353 ** 
## healthunhealthy:wave2 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) hlthnh wave2 
## helthnhlthy -0.357              
## wave2       -0.370  0.445       
## hlthnhlth:2  0.247 -0.686 -0.652

craving

brain ~ health x wave

craving signature expression for unhealthy foods decreased from wave 1 to 2; no change for healthy foods

tidytable

mod_health = lmer(dotProduct ~ health * wave + (1 | subjectID),
                  data = data_diss_craving,
                  control = lmerControl(optimizer = "bobyqa"))
table_model(mod_health)

term	b [95% CI]	df	t	p
(Intercept)	2.16 [1.63, 2.68]	471.12	8.08	< .001
health (unhealthy)	1.90 [1.41, 2.39]	14479.99	7.62	< .001
wave2	0.04 [-0.44, 0.52]	14551.18	0.16	.870
health (unhealthy) x wave2	-0.68 [-1.39, 0.02]	14360.84	-1.89	.060

plot

ggeffects::ggpredict(mod_health, terms = c("wave", "health")) %>%
  data.frame() %>%
  ggplot(aes(x, predicted, color = group, fill = group)) +
  geom_pointrange(aes(ymin = conf.low, ymax = conf.high)) +
  geom_line(aes(group = group)) +
  scale_color_manual(name = "", values = food) + 
  scale_fill_manual(name = "", values = food) + 
  labs(x = "\nwave", y = "predicted pattern expression value\n") + 
  dc_bw

model summary

summary(mod_health)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dotProduct ~ health * wave + (1 | subjectID)
##    Data: data_diss_craving
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 111327.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -9.4537 -0.6246 -0.0123  0.5962  7.8133 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  subjectID (Intercept)  11.53    3.395  
##  Residual              115.95   10.768  
## Number of obs: 14602, groups:  subjectID, 264
## 
## Fixed effects:
##                          Estimate  Std. Error          df t value
## (Intercept)               2.15833     0.26700   471.11984   8.084
## healthunhealthy           1.90140     0.24957 14479.98695   7.619
## wave2                     0.03922     0.24324 14551.17930   0.161
## healthunhealthy:wave2    -0.68064     0.35990 14360.83508  -1.891
##                                  Pr(>|t|)    
## (Intercept)           0.00000000000000534 ***
## healthunhealthy       0.00000000000002718 ***
## wave2                              0.8719    
## healthunhealthy:wave2              0.0586 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) hlthnh wave2 
## helthnhlthy -0.410              
## wave2       -0.416  0.445       
## hlthnhlth:2  0.280 -0.679 -0.656

bid ~ brain x wave

between-person expression: higher average expression is associated with lower bids

within-person expression: higher trial-level expression is associated with higher bids

intervention-related decreases in bid value aren weaker (i.e., more positive) for people who have higher expression average

tidy table

mod_bid = lmer(bid ~ dot_between * wave + dot_within * wave + (1 | subjectID),
               data = data_diss_craving,
               control = lmerControl(optimizer = "bobyqa"))
table_model(mod_bid)

term	b [95% CI]	df	t	p
(Intercept)	1.01 [0.98, 1.03]	418.93	79.54	< .001
between	0.00 [-0.02, 0.02]	4021.29	0.32	.750
wave2	-0.08 [-0.10, -0.07]	13471.71	-8.88	< .001
within	0.02 [0.01, 0.03]	13391.82	3.99	< .001
between x wave2	0.03 [0.00, 0.05]	12185.06	2.38	.020
wave2 x within	-0.00 [-0.02, 0.01]	13391.15	-0.42	.680

plot

by wave

vals = seq(-6, 6, .2)
ggeffects::ggpredict(mod_bid, terms = c("dot_between[vals]", "wave")) %>%
  data.frame() %>%
  mutate(type = "between-person") %>%
  bind_rows(ggeffects::ggpredict(mod_bid, terms = c("dot_within[vals]", "wave")) %>%
              data.frame() %>%
              mutate(type = "within-person")) %>%
  ggplot(aes(x, predicted, color = group, fill = group)) +
  geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .5, color = NA) +
  geom_line(aes(group = group)) +
  facet_grid(~type) +
  scale_color_manual(name = "wave", values = algorithm) + 
  scale_fill_manual(name = "wave", values = algorithm) + 
  labs(x = "\npattern expression value", y = "predicted bid value\n") + 
  dc_bw

by expression

ggeffects::ggpredict(mod_bid, terms = c("wave", "dot_between [-1, 0, 1]")) %>%
  data.frame() %>%
  mutate(type = "between-person") %>%
  bind_rows(ggeffects::ggpredict(mod_bid, terms = c("wave", "dot_within [-1, 0, 1]")) %>%
              data.frame() %>%
              mutate(type = "within-person")) %>%
  ggplot(aes(x, predicted, color = group, fill = group)) +
  geom_pointrange(aes(ymin = conf.low, ymax = conf.high)) +
  geom_line(aes(group = group)) +
  facet_grid(~type) +
  scale_color_manual(name = "expression", values = algorithm) + 
  scale_fill_manual(name = "expression", values = algorithm) + 
  labs(x = "\nwave", y = "predicted bid value\n") + 
  dc_bw

model summary

summary(mod_bid)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: bid ~ dot_between * wave + dot_within * wave + (1 | subjectID)
##    Data: data_diss_craving
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 15667.3
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -3.02066 -0.74653  0.05724  0.84822  2.36870 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  subjectID (Intercept) 0.02701  0.1643  
##  Residual              0.17641  0.4200  
## Number of obs: 13658, groups:  subjectID, 263
## 
## Fixed effects:
##                       Estimate   Std. Error           df t value
## (Intercept)           1.005976     0.012647   418.931406  79.543
## dot_between           0.003148     0.009880  4021.286016   0.319
## wave2                -0.083465     0.009395 13471.706246  -8.884
## dot_within            0.020157     0.005051 13391.816230   3.991
## dot_between:wave2     0.025741     0.010795 12185.055760   2.385
## wave2:dot_within     -0.003037     0.007318 13391.153601  -0.415
##                               Pr(>|t|)    
## (Intercept)       < 0.0000000000000002 ***
## dot_between                     0.7501    
## wave2             < 0.0000000000000002 ***
## dot_within                   0.0000663 ***
## dot_between:wave2               0.0171 *  
## wave2:dot_within                0.6781    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) dt_btw wave2  dt_wth dt_b:2
## dot_between -0.439                            
## wave2       -0.383  0.398                     
## dot_within   0.005 -0.002 -0.010              
## dt_btwn:wv2  0.254 -0.599 -0.607  0.006       
## wv2:dt_wthn -0.004  0.001  0.010 -0.690 -0.006

bid ~ brain x health x wave

no 3-way interactions

tidy table

mod_bid_health = lmer(bid ~ dot_between*health*wave + dot_within*health*wave + (1 + health | subjectID),
                      data = data_diss_craving,
                      control = lmerControl(optimizer = "bobyqa"))
table_model(mod_bid_health)

term	b [95% CI]	df	t	p
(Intercept)	1.01 [0.98, 1.04]	435.96	67.88	< .001
between	-0.01 [-0.03, 0.02]	3343.56	-0.59	.550
health (unhealthy)	-0.02 [-0.07, 0.02]	446.12	-1.05	.290
wave2	0.01 [-0.01, 0.04]	13110.40	1.20	.230
within	0.02 [0.00, 0.03]	13175.29	2.52	.010
between x health (unhealthy)	0.02 [-0.01, 0.06]	3152.78	1.19	.230
between x wave2	0.02 [-0.00, 0.05]	11369.25	1.69	.090
health (unhealthy) x wave2	-0.22 [-0.25, -0.18]	13018.35	-12.27	< .001
health (unhealthy) x within	0.00 [-0.01, 0.02]	13180.54	0.43	.670
wave2 x within	-0.00 [-0.02, 0.01]	13168.04	-0.40	.690
between x health (unhealthy) x wave2	0.00 [-0.04, 0.04]	11140.19	0.09	.930
health (unhealthy) x wave2 x within	0.02 [-0.01, 0.04]	13177.20	1.11	.270

plot

by wave

vals = seq(-6, 6, .2)
ggeffects::ggpredict(mod_bid_health, terms = c("dot_between[vals]", "wave", "health")) %>%
  data.frame() %>%
  mutate(type = "between-person") %>%
  bind_rows(ggeffects::ggpredict(mod_bid_health, terms = c("dot_within[vals]", "wave", "health")) %>%
              data.frame() %>%
              mutate(type = "within-person")) %>%
  ggplot(aes(x, predicted, color = group, fill = group)) +
  geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .5, color = NA) +
  geom_line(aes(group = group)) +
  facet_grid(type~facet) +
  scale_color_manual(name = "wave", values = algorithm) + 
  scale_fill_manual(name = "wave", values = algorithm) + 
  labs(x = "\npattern expression value", y = "predicted bid value\n") + 
  dc_bw

by expression

ggeffects::ggpredict(mod_bid_health, terms = c("wave", "dot_between [-1, 0, 1]", "health")) %>%
  data.frame() %>%
  mutate(type = "between-person") %>%
  bind_rows(ggeffects::ggpredict(mod_bid_health, terms = c("wave", "dot_within [-1, 0, 1]", "health")) %>%
              data.frame() %>%
              mutate(type = "within-person")) %>%
  ggplot(aes(x, predicted, color = group, fill = group)) +
  geom_pointrange(aes(ymin = conf.low, ymax = conf.high)) +
  geom_line(aes(group = group)) +
  facet_grid(type~facet) +
  scale_color_manual(name = "expression", values = algorithm) + 
  scale_fill_manual(name = "expression", values = algorithm) + 
  labs(x = "\nwave", y = "predicted bid value\n") + 
  dc_bw

model summary

summary(mod_bid_health)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: bid ~ dot_between * health * wave + dot_within * health * wave +  
##     (1 + health | subjectID)
##    Data: data_diss_craving
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 14370.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.3000 -0.6499  0.0261  0.7964  3.1144 
## 
## Random effects:
##  Groups    Name            Variance Std.Dev. Corr 
##  subjectID (Intercept)     0.03523  0.1877        
##            healthunhealthy 0.06978  0.2642   -0.52
##  Residual                  0.15432  0.3928        
## Number of obs: 13658, groups:  subjectID, 263
## 
## Fixed effects:
##                                       Estimate   Std. Error           df
## (Intercept)                           1.010852     0.014892   435.957912
## dot_between                          -0.007152     0.012032  3343.557978
## healthunhealthy                      -0.022811     0.021659   446.124797
## wave2                                 0.014102     0.011729 13110.403741
## dot_within                            0.016217     0.006423 13175.286446
## dot_between:healthunhealthy           0.021546     0.018110  3152.775598
## dot_between:wave2                     0.022457     0.013292 11369.253589
## healthunhealthy:wave2                -0.216925     0.017686 13018.351401
## healthunhealthy:dot_within            0.004105     0.009605 13180.536599
## wave2:dot_within                     -0.003660     0.009260 13168.036161
## dot_between:healthunhealthy:wave2     0.001789     0.020255 11140.186442
## healthunhealthy:wave2:dot_within      0.015415     0.013900 13177.202170
##                                   t value            Pr(>|t|)    
## (Intercept)                        67.879 <0.0000000000000002 ***
## dot_between                        -0.594              0.5523    
## healthunhealthy                    -1.053              0.2928    
## wave2                               1.202              0.2293    
## dot_within                          2.525              0.0116 *  
## dot_between:healthunhealthy         1.190              0.2343    
## dot_between:wave2                   1.689              0.0912 .  
## healthunhealthy:wave2             -12.265 <0.0000000000000002 ***
## healthunhealthy:dot_within          0.427              0.6692    
## wave2:dot_within                   -0.395              0.6926    
## dot_between:healthunhealthy:wave2   0.088              0.9296    
## healthunhealthy:wave2:dot_within    1.109              0.2674    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) dt_btw hlthnh wave2  dt_wth dt_bt: dt_b:2 hlth:2 hlth:_
## dot_between -0.456                                                        
## helthnhlthy -0.567  0.296                                                 
## wave2       -0.403  0.394  0.274                                          
## dot_within   0.046 -0.017 -0.032 -0.063                                   
## dt_btwn:hlt  0.287 -0.630 -0.468 -0.254  0.011                            
## dt_btwn:wv2  0.267 -0.603 -0.179 -0.603  0.021  0.391                     
## hlthnhlth:2  0.264 -0.254 -0.421 -0.658  0.041  0.401  0.394              
## hlthnhlth:_ -0.031  0.012 -0.004  0.042 -0.669  0.002 -0.014  0.004       
## wv2:dt_wthn -0.033  0.015  0.023  0.070 -0.693 -0.009 -0.023 -0.046  0.464
## dt_btwn:h:2 -0.172  0.387  0.276  0.390 -0.013 -0.605 -0.646 -0.605  0.001
## hlthnhl:2:_  0.022 -0.010  0.004 -0.047  0.462 -0.004  0.015 -0.006 -0.690
##             wv2:d_ dt_::2
## dot_between              
## helthnhlthy              
## wave2                    
## dot_within               
## dt_btwn:hlt              
## dt_btwn:wv2              
## hlthnhlth:2              
## hlthnhlth:_              
## wv2:dt_wthn              
## dt_btwn:h:2  0.015       
## hlthnhl:2:_ -0.666 -0.001

brain ~ liking x health

higher session 0 liking ratings are associated with stronger signature expression

this relationship is stronger for unhealthy foods

tidy table

mod_liking = lmer(dotProduct ~ liking_rating * health + (1 | subjectID),
                  data = data_diss_liking_craving,
                  control = lmerControl(optimizer = "bobyqa"))
table_model(mod_liking)

term	b [95% CI]	df	t	p
(Intercept)	-1.24 [-1.84, -0.65]	913.41	-4.10	< .001
liking	0.96 [0.81, 1.11]	30704.55	12.55	< .001
health (unhealthy)	0.29 [-0.27, 0.84]	30652.14	1.01	.310
liking x health (unhealthy)	0.36 [0.15, 0.56]	30681.39	3.39	< .001

plot

ggeffects::ggpredict(mod_liking, terms = c("liking_rating", "health")) %>%
  data.frame() %>%
  ggplot(aes(x, predicted, color = group, fill = group)) +
  geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .5, color = NA) +
  geom_line() +
  scale_color_manual(name = "", values = food) + 
  scale_fill_manual(name = "", values = food) + 
  labs(x = "\nliking rating (session 0)", y = "signature expression\n") + 
  dc_bw

model summary

summary(mod_liking)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dotProduct ~ liking_rating * health + (1 | subjectID)
##    Data: data_diss_liking_craving
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 234668.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -9.3602 -0.6174 -0.0126  0.5977 12.5332 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  subjectID (Intercept)  11.79    3.433  
##  Residual              118.37   10.880  
## Number of obs: 30742, groups:  subjectID, 264
## 
## Fixed effects:
##                                 Estimate Std. Error         df t value
## (Intercept)                      -1.2413     0.3029   913.4073  -4.098
## liking_rating                     0.9617     0.0766 30704.5523  12.555
## healthunhealthy                   0.2856     0.2840 30652.1392   1.006
## liking_rating:healthunhealthy     0.3577     0.1056 30681.3854   3.386
##                                           Pr(>|t|)    
## (Intercept)                              0.0000454 ***
## liking_rating                 < 0.0000000000000002 ***
## healthunhealthy                            0.31465    
## liking_rating:healthunhealthy              0.00071 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) lkng_r hlthnh
## liking_rtng -0.653              
## helthnhlthy -0.541  0.690       
## lkng_rtng:h  0.468 -0.716 -0.897

Koban craving

brain ~ health x wave

craving signature expression for unhealthy foods decreased from wave 1 to 2; no change for healthy foods

tidytable

mod_health = lmer(dotProduct ~ health * wave + (1 | subjectID),
                  data = data_diss_craving_koban,
                  control = lmerControl(optimizer = "bobyqa"))
table_model(mod_health)

term	b [95% CI]	df	t	p
(Intercept)	2.56 [2.20, 2.91]	408.59	14.09	< .001
health (unhealthy)	-0.46 [-0.75, -0.16]	14451.25	-3.03	< .001
wave2	-0.09 [-0.37, 0.20]	14519.94	-0.58	.560
health (unhealthy) x wave2	0.05 [-0.37, 0.48]	14354.84	0.25	.800

plot

ggeffects::ggpredict(mod_health, terms = c("wave", "health")) %>%
  data.frame() %>%
  ggplot(aes(x, predicted, color = group, fill = group)) +
  geom_pointrange(aes(ymin = conf.low, ymax = conf.high)) +
  geom_line(aes(group = group)) +
  scale_color_manual(name = "", values = food) + 
  scale_fill_manual(name = "", values = food) + 
  labs(x = "\nwave", y = "predicted pattern expression value\n") + 
  dc_bw

model summary

summary(mod_health)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dotProduct ~ health * wave + (1 | subjectID)
##    Data: data_diss_craving_koban
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 96747.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.8357 -0.6117 -0.0035  0.5975  6.2191 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  subjectID (Intercept)  6.025   2.455   
##  Residual              42.476   6.517   
## Number of obs: 14602, groups:  subjectID, 264
## 
## Fixed effects:
##                          Estimate  Std. Error          df t value
## (Intercept)               2.55766     0.18154   408.58538  14.088
## healthunhealthy          -0.45849     0.15118 14451.24784  -3.033
## wave2                    -0.08524     0.14743 14519.94297  -0.578
## healthunhealthy:wave2     0.05422     0.21786 14354.84216   0.249
##                                   Pr(>|t|)    
## (Intercept)           < 0.0000000000000002 ***
## healthunhealthy                    0.00243 ** 
## wave2                              0.56315    
## healthunhealthy:wave2              0.80346    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) hlthnh wave2 
## helthnhlthy -0.365              
## wave2       -0.370  0.445       
## hlthnhlth:2  0.249 -0.679 -0.655

bid ~ brain x wave

between-person expression: higher average expression is associated with lower bids

within-person expression: higher trial-level expression is associated with higher bids

intervention-related decreases in bid value aren’t moderated by signature expression

tidy table

mod_bid = lmer(bid ~ dot_between * wave + dot_within * wave + (1 | subjectID),
               data = data_diss_craving_koban,
               control = lmerControl(optimizer = "bobyqa"))
table_model(mod_bid)

term	b [95% CI]	df	t	p
(Intercept)	0.99 [0.97, 1.02]	446.88	75.63	< .001
between	0.03 [0.00, 0.06]	2829.76	2.24	.020
wave2	-0.08 [-0.10, -0.06]	13561.93	-7.49	< .001
within	0.06 [0.05, 0.07]	13392.60	12.89	< .001
between x wave2	0.01 [-0.02, 0.04]	13311.61	0.74	.460
wave2 x within	0.01 [-0.00, 0.03]	13389.64	1.67	.090

plot

by wave

vals = seq(-6, 6, .2)
ggeffects::ggpredict(mod_bid, terms = c("dot_between[vals]", "wave")) %>%
  data.frame() %>%
  mutate(type = "between-person") %>%
  bind_rows(ggeffects::ggpredict(mod_bid, terms = c("dot_within[vals]", "wave")) %>%
              data.frame() %>%
              mutate(type = "within-person")) %>%
  ggplot(aes(x, predicted, color = group, fill = group)) +
  geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .5, color = NA) +
  geom_line(aes(group = group)) +
  facet_grid(~type) +
  scale_color_manual(name = "wave", values = algorithm) + 
  scale_fill_manual(name = "wave", values = algorithm) + 
  labs(x = "\npattern expression value", y = "predicted bid value\n") + 
  dc_bw

by expression

ggeffects::ggpredict(mod_bid, terms = c("wave", "dot_between [-1, 0, 1]")) %>%
  data.frame() %>%
  mutate(type = "between-person") %>%
  bind_rows(ggeffects::ggpredict(mod_bid, terms = c("wave", "dot_within [-1, 0, 1]")) %>%
              data.frame() %>%
              mutate(type = "within-person")) %>%
  ggplot(aes(x, predicted, color = group, fill = group)) +
  geom_pointrange(aes(ymin = conf.low, ymax = conf.high)) +
  geom_line(aes(group = group)) +
  facet_grid(~type) +
  scale_color_manual(name = "expression", values = algorithm) + 
  scale_fill_manual(name = "expression", values = algorithm) + 
  labs(x = "\nwave", y = "predicted bid value\n") + 
  dc_bw

model summary

summary(mod_bid)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: bid ~ dot_between * wave + dot_within * wave + (1 | subjectID)
##    Data: data_diss_craving_koban
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 15318.4
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -3.14700 -0.71891  0.06117  0.80652  2.69710 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  subjectID (Intercept) 0.02714  0.1647  
##  Residual              0.17189  0.4146  
## Number of obs: 13658, groups:  subjectID, 263
## 
## Fixed effects:
##                       Estimate   Std. Error           df t value
## (Intercept)           0.992776     0.013127   446.883628  75.629
## dot_between           0.032974     0.014694  2829.756726   2.244
## wave2                -0.075517     0.010086 13561.931744  -7.487
## dot_within            0.064190     0.004979 13392.603000  12.893
## dot_between:wave2     0.011130     0.015058 13311.608959   0.739
## wave2:dot_within      0.012072     0.007210 13389.640374   1.674
##                               Pr(>|t|)    
## (Intercept)       < 0.0000000000000002 ***
## dot_between                     0.0249 *  
## wave2               0.0000000000000746 ***
## dot_within        < 0.0000000000000002 ***
## dot_between:wave2               0.4598    
## wave2:dot_within                0.0941 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) dt_btw wave2  dt_wth dt_b:2
## dot_between -0.502                            
## wave2       -0.357  0.338                     
## dot_within  -0.001 -0.002  0.000              
## dt_btwn:wv2  0.239 -0.474 -0.683  0.003       
## wv2:dt_wthn  0.001  0.001 -0.001 -0.690 -0.004

bid ~ brain x health x wave

relationship between within-person expression and bid values for unhealthy foods is slightly stronger at wave 2

tidy table

mod_bid_health = lmer(bid ~ dot_between*health*wave + dot_within*health*wave + (1 + health | subjectID),
                      data = data_diss_craving_koban,
                      control = lmerControl(optimizer = "bobyqa"))
table_model(mod_bid_health)

term	b [95% CI]	df	t	p
(Intercept)	1.00 [0.97, 1.03]	471.22	64.30	< .001
between	0.02 [-0.02, 0.05]	2318.85	0.95	.340
health (unhealthy)	-0.02 [-0.07, 0.02]	478.71	-1.12	.260
wave2	0.02 [-0.00, 0.05]	13143.52	1.60	.110
within	0.06 [0.05, 0.08]	13175.26	10.21	< .001
between x health (unhealthy)	0.05 [-0.00, 0.10]	2181.45	1.84	.070
between x wave2	0.01 [-0.02, 0.05]	12713.84	0.75	.450
health (unhealthy) x wave2	-0.21 [-0.25, -0.18]	13158.94	-11.31	< .001
health (unhealthy) x within	-0.01 [-0.03, 0.01]	13183.43	-0.72	.470
wave2 x within	-0.01 [-0.03, 0.01]	13178.10	-0.97	.330
between x health (unhealthy) x wave2	-0.01 [-0.06, 0.05]	12680.43	-0.29	.770
health (unhealthy) x wave2 x within	0.03 [0.00, 0.05]	13182.21	1.98	.050

plot

by wave

vals = seq(-6, 6, .2)
ggeffects::ggpredict(mod_bid_health, terms = c("dot_between[vals]", "wave", "health")) %>%
  data.frame() %>%
  mutate(type = "between-person") %>%
  bind_rows(ggeffects::ggpredict(mod_bid_health, terms = c("dot_within[vals]", "wave", "health")) %>%
              data.frame() %>%
              mutate(type = "within-person")) %>%
  ggplot(aes(x, predicted, color = group, fill = group)) +
  geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .5, color = NA) +
  geom_line(aes(group = group)) +
  facet_grid(type~facet) +
  scale_color_manual(name = "wave", values = algorithm) + 
  scale_fill_manual(name = "wave", values = algorithm) + 
  labs(x = "\npattern expression value", y = "predicted bid value\n") + 
  dc_bw

by expression

ggeffects::ggpredict(mod_bid_health, terms = c("wave", "dot_between [-1, 0, 1]", "health")) %>%
  data.frame() %>%
  mutate(type = "between-person") %>%
  bind_rows(ggeffects::ggpredict(mod_bid_health, terms = c("wave", "dot_within [-1, 0, 1]", "health")) %>%
              data.frame() %>%
              mutate(type = "within-person")) %>%
  ggplot(aes(x, predicted, color = group, fill = group)) +
  geom_pointrange(aes(ymin = conf.low, ymax = conf.high)) +
  geom_line(aes(group = group)) +
  facet_grid(type~facet) +
  scale_color_manual(name = "expression", values = algorithm) + 
  scale_fill_manual(name = "expression", values = algorithm) + 
  labs(x = "\nwave", y = "predicted bid value\n") + 
  dc_bw

model summary

summary(mod_bid_health)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: bid ~ dot_between * health * wave + dot_within * health * wave +  
##     (1 + health | subjectID)
##    Data: data_diss_craving_koban
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 14066.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.3873 -0.6517  0.0381  0.7724  3.4361 
## 
## Random effects:
##  Groups    Name            Variance Std.Dev. Corr 
##  subjectID (Intercept)     0.03476  0.1864        
##            healthunhealthy 0.06641  0.2577   -0.51
##  Residual                  0.15097  0.3886        
## Number of obs: 13658, groups:  subjectID, 263
## 
## Fixed effects:
##                                       Estimate   Std. Error           df
## (Intercept)                           0.996071     0.015491   471.219429
## dot_between                           0.017085     0.018033  2318.851659
## healthunhealthy                      -0.024862     0.022145   478.709671
## wave2                                 0.020386     0.012719 13143.516878
## dot_within                            0.063501     0.006221 13175.256256
## dot_between:healthunhealthy           0.048669     0.026490  2181.448019
## dot_between:wave2                     0.014189     0.018921 12713.838750
## healthunhealthy:wave2                -0.214112     0.018930 13158.938835
## healthunhealthy:dot_within           -0.006881     0.009496 13183.427781
## wave2:dot_within                     -0.008873     0.009111 13178.097518
## dot_between:healthunhealthy:wave2    -0.008144     0.028214 12680.425400
## healthunhealthy:wave2:dot_within      0.027222     0.013736 13182.213748
##                                   t value            Pr(>|t|)    
## (Intercept)                        64.298 <0.0000000000000002 ***
## dot_between                         0.947              0.3435    
## healthunhealthy                    -1.123              0.2621    
## wave2                               1.603              0.1090    
## dot_within                         10.208 <0.0000000000000002 ***
## dot_between:healthunhealthy         1.837              0.0663 .  
## dot_between:wave2                   0.750              0.4533    
## healthunhealthy:wave2             -11.311 <0.0000000000000002 ***
## healthunhealthy:dot_within         -0.725              0.4687    
## wave2:dot_within                   -0.974              0.3301    
## dot_between:healthunhealthy:wave2  -0.289              0.7729    
## healthunhealthy:wave2:dot_within    1.982              0.0475 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) dt_btw hlthnh wave2  dt_wth dt_bt: dt_b:2 hlth:2 hlth:_
## dot_between -0.529                                                        
## helthnhlthy -0.573  0.345                                                 
## wave2       -0.391  0.368  0.271                                          
## dot_within  -0.005 -0.015  0.003  0.010                                   
## dt_btwn:hlt  0.335 -0.633 -0.534 -0.245  0.010                            
## dt_btwn:wv2  0.265 -0.500 -0.182 -0.688  0.008  0.335                     
## hlthnhlth:2  0.261 -0.244 -0.393 -0.667 -0.007  0.339  0.457              
## hlthnhlth:_  0.003  0.010  0.000 -0.007 -0.655  0.004 -0.005 -0.003       
## wv2:dt_wthn  0.004  0.011 -0.003 -0.012 -0.682 -0.007 -0.022  0.008  0.447
## dt_btwn:h:2 -0.176  0.332  0.262  0.456 -0.006 -0.488 -0.664 -0.680  0.001
## hlthnhl:2:_ -0.002 -0.008  0.001  0.008  0.452 -0.003  0.014  0.002 -0.690
##             wv2:d_ dt_::2
## dot_between              
## helthnhlthy              
## wave2                    
## dot_within               
## dt_btwn:hlt              
## dt_btwn:wv2              
## hlthnhlth:2              
## hlthnhlth:_              
## wv2:dt_wthn              
## dt_btwn:h:2  0.014       
## hlthnhl:2:_ -0.664  0.000

brain ~ liking x health

higher session 0 liking ratings are associated with stronger signature expression

tidy table

mod_liking = lmer(dotProduct ~ liking_rating * health + (1 | subjectID),
                  data = data_diss_liking_craving_koban,
                  control = lmerControl(optimizer = "bobyqa"))
table_model(mod_liking)

term	b [95% CI]	df	t	p
(Intercept)	-0.20 [-0.58, 0.19]	700.58	-0.99	.320
liking	0.75 [0.66, 0.84]	30663.63	16.39	< .001
health (unhealthy)	-0.31 [-0.64, 0.03]	30613.14	-1.81	.070
liking x health (unhealthy)	-0.03 [-0.15, 0.10]	30639.92	-0.41	.690

plot

ggeffects::ggpredict(mod_liking, terms = c("liking_rating", "health")) %>%
  data.frame() %>%
  ggplot(aes(x, predicted, color = group, fill = group)) +
  geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .5, color = NA) +
  geom_line() +
  scale_color_manual(name = "", values = food) + 
  scale_fill_manual(name = "", values = food) + 
  labs(x = "\nliking rating (session 0)", y = "signature expression\n") + 
  dc_bw

model summary

summary(mod_liking)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dotProduct ~ liking_rating * health + (1 | subjectID)
##    Data: data_diss_liking_craving_koban
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 202918.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -9.1535 -0.6141  0.0002  0.6113  7.4154 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  subjectID (Intercept)  5.876   2.424   
##  Residual              42.024   6.483   
## Number of obs: 30742, groups:  subjectID, 264
## 
## Fixed effects:
##                                  Estimate  Std. Error          df t value
## (Intercept)                      -0.19566     0.19748   700.58406  -0.991
## liking_rating                     0.74849     0.04568 30663.62655  16.387
## healthunhealthy                  -0.30627     0.16929 30613.13989  -1.809
## liking_rating:healthunhealthy    -0.02552     0.06297 30639.92428  -0.405
##                                          Pr(>|t|)    
## (Intercept)                                0.3222    
## liking_rating                 <0.0000000000000002 ***
## healthunhealthy                            0.0704 .  
## liking_rating:healthunhealthy              0.6852    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) lkng_r hlthnh
## liking_rtng -0.597              
## helthnhlthy -0.494  0.690       
## lkng_rtng:h  0.428 -0.716 -0.897

craving regulation

brain ~ health x wave

signature expression was stronger for unhealthy foods

signature expression for unhealthy foods increase after the intervention

tidytable

mod_health = lmer(dotProduct ~ health * wave + (1 + health | subjectID),
                  data = data_diss_regulation,
                  control = lmerControl(optimizer = "bobyqa"))
table_model(mod_health)

term	b [95% CI]	df	t	p
(Intercept)	-5.76 [-6.25, -5.27]	337.99	-23.04	< .001
health (unhealthy)	1.44 [1.05, 1.82]	827.24	7.26	< .001
wave2	-0.28 [-0.64, 0.09]	14354.61	-1.46	.140
health (unhealthy) x wave2	1.16 [0.61, 1.70]	14029.99	4.16	< .001

plot

ggeffects::ggpredict(mod_health, terms = c("wave", "health")) %>%
  data.frame() %>%
  ggplot(aes(x, predicted, color = group, fill = group)) +
  geom_pointrange(aes(ymin = conf.low, ymax = conf.high)) +
  geom_line(aes(group = group)) +
  scale_color_manual(name = "", values = food) + 
  scale_fill_manual(name = "", values = food) + 
  labs(x = "\nwave", y = "predicted pattern expression value\n") + 
  dc_bw

model summary

summary(mod_health)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dotProduct ~ health * wave + (1 + health | subjectID)
##    Data: data_diss_regulation
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 103891.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.4832 -0.6325  0.0007  0.6334  6.0639 
## 
## Random effects:
##  Groups    Name            Variance Std.Dev. Corr 
##  subjectID (Intercept)     12.1518  3.4859        
##            healthunhealthy  0.4621  0.6798   -0.19
##  Residual                  68.9765  8.3052        
## Number of obs: 14602, groups:  subjectID, 264
## 
## Fixed effects:
##                         Estimate Std. Error         df t value
## (Intercept)              -5.7627     0.2501   337.9910 -23.042
## healthunhealthy           1.4352     0.1976   827.2382   7.262
## wave2                    -0.2754     0.1882 14354.6147  -1.463
## healthunhealthy:wave2     1.1576     0.2781 14029.9874   4.162
##                                   Pr(>|t|)    
## (Intercept)           < 0.0000000000000002 ***
## healthunhealthy          0.000000000000879 ***
## wave2                                0.143    
## healthunhealthy:wave2    0.000031712713529 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) hlthnh wave2 
## helthnhlthy -0.365              
## wave2       -0.342  0.433       
## hlthnhlth:2  0.230 -0.661 -0.656

bid ~ brain x wave

between-person expression: higher average expression is associated with lower bids

within-person expression: higher trial-level expression is associated with lower bids

intervention-related decreases in bid value are stronger (i.e., more negative) on trials with higher than average expression

tidy table

mod_bid = lmer(bid ~ dot_between * wave + dot_within * wave + (1 + dot_within | subjectID),
               data = data_diss_regulation,
               control = lmerControl(optimizer = "bobyqa"))
table_model(mod_bid)

term	b [95% CI]	df	t	p
(Intercept)	0.96 [0.93, 0.99]	710.97	61.17	< .001
between	-0.05 [-0.07, -0.03]	2985.94	-4.62	< .001
wave2	-0.08 [-0.11, -0.06]	13281.52	-6.53	< .001
within	-0.01 [-0.02, -0.00]	715.31	-2.08	.040
between x wave2	-0.02 [-0.04, 0.00]	13232.89	-1.57	.120
wave2 x within	-0.03 [-0.04, -0.02]	12848.54	-4.10	< .001

plot

by wave

vals = seq(-6, 6, .2)
ggeffects::ggpredict(mod_bid, terms = c("dot_between[vals]", "wave")) %>%
  data.frame() %>%
  mutate(type = "between-person") %>%
  bind_rows(ggeffects::ggpredict(mod_bid, terms = c("dot_within[vals]", "wave")) %>%
              data.frame() %>%
              mutate(type = "within-person")) %>%
  ggplot(aes(x, predicted, color = group, fill = group)) +
  geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .5, color = NA) +
  geom_line(aes(group = group)) +
  facet_grid(~type) +
  scale_color_manual(name = "wave", values = algorithm) + 
  scale_fill_manual(name = "wave", values = algorithm) + 
  labs(x = "\npattern expression value", y = "predicted bid value\n") + 
  dc_bw

by expression

ggeffects::ggpredict(mod_bid, terms = c("wave", "dot_between [-1, 0, 1]")) %>%
  data.frame() %>%
  mutate(type = "between-person") %>%
  bind_rows(ggeffects::ggpredict(mod_bid, terms = c("wave", "dot_within [-1, 0, 1]")) %>%
              data.frame() %>%
              mutate(type = "within-person")) %>%
  ggplot(aes(x, predicted, color = group, fill = group)) +
  geom_pointrange(aes(ymin = conf.low, ymax = conf.high)) +
  geom_line(aes(group = group)) +
  facet_grid(~type) +
  scale_color_manual(name = "expression", values = algorithm) + 
  scale_fill_manual(name = "expression", values = algorithm) + 
  labs(x = "\nwave", y = "predicted bid value\n") + 
  dc_bw

model summary

summary(mod_bid)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: bid ~ dot_between * wave + dot_within * wave + (1 + dot_within |  
##     subjectID)
##    Data: data_diss_regulation
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 15574.6
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -3.12181 -0.71888  0.04117  0.83742  2.51205 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev. Corr 
##  subjectID (Intercept) 0.026395 0.16246       
##            dot_within  0.001269 0.03562  -0.03
##  Residual              0.174225 0.41740       
## Number of obs: 13658, groups:  subjectID, 263
## 
## Fixed effects:
##                       Estimate   Std. Error           df t value
## (Intercept)           0.957909     0.015659   710.974439  61.173
## dot_between          -0.049696     0.010747  2985.939936  -4.624
## wave2                -0.084906     0.013006 13281.524098  -6.528
## dot_within           -0.011547     0.005552   715.307711  -2.080
## dot_between:wave2    -0.016327     0.010367 13232.894142  -1.575
## wave2:dot_within     -0.030190     0.007357 12848.538891  -4.103
##                               Pr(>|t|)    
## (Intercept)       < 0.0000000000000002 ***
## dot_between            0.0000039187623 ***
## wave2                  0.0000000000689 ***
## dot_within                      0.0379 *  
## dot_between:wave2               0.1153    
## wave2:dot_within       0.0000409704321 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) dt_btw wave2  dt_wth dt_b:2
## dot_between  0.696                            
## wave2       -0.450 -0.461                     
## dot_within   0.000 -0.003 -0.008              
## dt_btwn:wv2 -0.377 -0.531  0.821  0.003       
## wv2:dt_wthn -0.005  0.003  0.007 -0.630 -0.004

bid ~ brain x health x wave

relationship between average (i.e., between-person) expression and bid values for unhealthy foods is slightly stronger (i.e., more negative) at wave 2

tidy table

mod_bid_health = lmer(bid ~ dot_between*health*wave + dot_within*health*wave + (1 + health | subjectID),
                      data = data_diss_regulation,
                      control = lmerControl(optimizer = "bobyqa"))
table_model(mod_bid_health)

term	b [95% CI]	df	t	p
(Intercept)	0.98 [0.94, 1.01]	741.28	52.01	< .001
between	-0.03 [-0.05, -0.00]	2657.00	-2.09	.040
health (unhealthy)	-0.05 [-0.11, 0.00]	731.50	-1.89	.060
wave2	0.03 [-0.00, 0.06]	13101.43	1.68	.090
within	-0.01 [-0.03, -0.00]	13195.64	-2.27	.020
between x health (unhealthy)	-0.05 [-0.09, -0.01]	2251.14	-2.42	.020
between x wave2	0.00 [-0.02, 0.03]	13005.95	0.17	.860
health (unhealthy) x wave2	-0.25 [-0.30, -0.20]	13012.70	-10.27	< .001
health (unhealthy) x within	0.01 [-0.01, 0.03]	13195.50	1.09	.270
wave2 x within	-0.01 [-0.03, 0.01]	13188.09	-1.31	.190
between x health (unhealthy) x wave2	-0.04 [-0.08, -0.00]	12907.11	-2.03	.040
health (unhealthy) x wave2 x within	0.00 [-0.02, 0.03]	13189.60	0.18	.860

plot

by wave

vals = seq(-6, 6, .2)
ggeffects::ggpredict(mod_bid_health, terms = c("dot_between[vals]", "wave", "health")) %>%
  data.frame() %>%
  mutate(type = "between-person") %>%
  bind_rows(ggeffects::ggpredict(mod_bid_health, terms = c("dot_within[vals]", "wave", "health")) %>%
              data.frame() %>%
              mutate(type = "within-person")) %>%
  ggplot(aes(x, predicted, color = group, fill = group)) +
  geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .5, color = NA) +
  geom_line(aes(group = group)) +
  facet_grid(type~facet) +
  scale_color_manual(name = "wave", values = algorithm) + 
  scale_fill_manual(name = "wave", values = algorithm) + 
  labs(x = "\npattern expression value", y = "predicted bid value\n") + 
  dc_bw

by expression

ggeffects::ggpredict(mod_bid_health, terms = c("wave", "dot_between [-1, 0, 1]", "health")) %>%
  data.frame() %>%
  mutate(type = "between-person") %>%
  bind_rows(ggeffects::ggpredict(mod_bid_health, terms = c("wave", "dot_within [-1, 0, 1]", "health")) %>%
              data.frame() %>%
              mutate(type = "within-person")) %>%
  ggplot(aes(x, predicted, color = group, fill = group)) +
  geom_pointrange(aes(ymin = conf.low, ymax = conf.high)) +
  geom_line(aes(group = group)) +
  facet_grid(type~facet) +
  scale_color_manual(name = "expression", values = algorithm) + 
  scale_fill_manual(name = "expression", values = algorithm) + 
  labs(x = "\nwave", y = "predicted bid value\n") + 
  dc_bw

model summary

summary(mod_bid_health)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: bid ~ dot_between * health * wave + dot_within * health * wave +  
##     (1 + health | subjectID)
##    Data: data_diss_regulation
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 14327.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.3293 -0.6472  0.0299  0.7941  3.2008 
## 
## Random effects:
##  Groups    Name            Variance Std.Dev. Corr 
##  subjectID (Intercept)     0.03496  0.1870        
##            healthunhealthy 0.06900  0.2627   -0.52
##  Residual                  0.15390  0.3923        
## Number of obs: 13658, groups:  subjectID, 263
## 
## Fixed effects:
##                                       Estimate   Std. Error           df
## (Intercept)                           0.976619     0.018779   741.284314
## dot_between                          -0.027408     0.013115  2656.999017
## healthunhealthy                      -0.052059     0.027555   731.495646
## wave2                                 0.027691     0.016485 13101.428526
## dot_within                           -0.014512     0.006396 13195.640117
## dot_between:healthunhealthy          -0.047530     0.019633  2251.136604
## dot_between:wave2                     0.002245     0.013158 13005.950314
## healthunhealthy:wave2                -0.252058     0.024548 13012.699222
## healthunhealthy:dot_within            0.010588     0.009687 13195.497277
## wave2:dot_within                     -0.012215     0.009357 13188.088563
## dot_between:healthunhealthy:wave2    -0.039532     0.019484 12907.107768
## healthunhealthy:wave2:dot_within      0.002515     0.014027 13189.603229
##                                   t value            Pr(>|t|)    
## (Intercept)                        52.006 <0.0000000000000002 ***
## dot_between                        -2.090              0.0367 *  
## healthunhealthy                    -1.889              0.0593 .  
## wave2                               1.680              0.0930 .  
## dot_within                         -2.269              0.0233 *  
## dot_between:healthunhealthy        -2.421              0.0156 *  
## dot_between:wave2                   0.171              0.8645    
## healthunhealthy:wave2             -10.268 <0.0000000000000002 ***
## healthunhealthy:dot_within          1.093              0.2744    
## wave2:dot_within                   -1.305              0.1918    
## dot_between:healthunhealthy:wave2  -2.029              0.0425 *  
## healthunhealthy:wave2:dot_within    0.179              0.8577    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) dt_btw hlthnh wave2  dt_wth dt_bt: dt_b:2 hlth:2 hlth:_
## dot_between  0.711                                                        
## helthnhlthy -0.593 -0.455                                                 
## wave2       -0.462 -0.463  0.311                                          
## dot_within   0.035  0.001 -0.024 -0.031                                   
## dt_btwn:hlt -0.446 -0.626  0.722  0.304 -0.001                            
## dt_btwn:wv2 -0.384 -0.533  0.259  0.822  0.009  0.351                     
## hlthnhlth:2  0.306  0.304 -0.480 -0.667  0.021 -0.470 -0.547              
## hlthnhlth:_ -0.024 -0.002 -0.001  0.021 -0.661 -0.005 -0.006 -0.002       
## wv2:dt_wthn -0.028 -0.005  0.018  0.071 -0.683  0.003  0.001 -0.048  0.451
## dt_btwn:h:2  0.255  0.354 -0.403 -0.550 -0.006 -0.546 -0.670  0.818  0.002
## hlthnhl:2:_  0.019  0.004  0.002 -0.047  0.456  0.006  0.000 -0.008 -0.689
##             wv2:d_ dt_::2
## dot_between              
## helthnhlthy              
## wave2                    
## dot_within               
## dt_btwn:hlt              
## dt_btwn:wv2              
## hlthnhlth:2              
## hlthnhlth:_              
## wv2:dt_wthn              
## dt_btwn:h:2  0.000       
## hlthnhl:2:_ -0.667 -0.005

brain ~ liking x health

higher session 0 liking ratings are associated with stronger signature expression for unhealthy foods

tidy table

mod_liking = lmer(dotProduct ~ liking_rating * health + (1 | subjectID),
                  data = data_diss_liking_regulation,
                  control = lmerControl(optimizer = "bobyqa"))
table_model(mod_liking)

term	b [95% CI]	df	t	p
(Intercept)	-5.99 [-6.52, -5.46]	628.47	-22.21	< .001
liking	0.03 [-0.09, 0.15]	30643.39	0.51	.610
health (unhealthy)	0.82 [0.39, 1.25]	30596.84	3.75	< .001
liking x health (unhealthy)	0.33 [0.17, 0.49]	30621.16	4.05	< .001

plot

ggeffects::ggpredict(mod_liking, terms = c("liking_rating", "health")) %>%
  data.frame() %>%
  ggplot(aes(x, predicted, color = group, fill = group)) +
  geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .5, color = NA) +
  geom_line() +
  scale_color_manual(name = "", values = food) + 
  scale_fill_manual(name = "", values = food) + 
  labs(x = "\nliking rating (session 0)", y = "signature expression\n") + 
  dc_bw

model summary

summary(mod_liking)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dotProduct ~ liking_rating * health + (1 | subjectID)
##    Data: data_diss_liking_regulation
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 218918.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.5300 -0.6350  0.0014  0.6323  6.4918 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  subjectID (Intercept) 11.75    3.427   
##  Residual              70.62    8.404   
## Number of obs: 30742, groups:  subjectID, 264
## 
## Fixed effects:
##                                  Estimate  Std. Error          df t value
## (Intercept)                      -5.98759     0.26954   628.46993 -22.214
## liking_rating                     0.03019     0.05923 30643.38679   0.510
## healthunhealthy                   0.82354     0.21951 30596.83518   3.752
## liking_rating:healthunhealthy     0.33085     0.08166 30621.15925   4.052
##                                           Pr(>|t|)    
## (Intercept)                   < 0.0000000000000002 ***
## liking_rating                             0.610241    
## healthunhealthy                           0.000176 ***
## liking_rating:healthunhealthy             0.000051 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) lkng_r hlthnh
## liking_rtng -0.568              
## helthnhlthy -0.470  0.690       
## lkng_rtng:h  0.407 -0.716 -0.897

DEV WTP betaseries

Dani Cosme

2023-02-02

prep

load packages

define aesthetics

define functions

load and tidy task data

check responses

recode and exclude

load mean intensity values

load dot products

join intensities and dots

exclude outliers and standardize

merge data

descriptives

number of participants

bids

across waves

by wave

session 0 liking ratings

correlations among signatures

craving & Koban craving

craving & craving regulation

Koban craving & craving regulation

visualize raw data

bids only

across waves

by wave

bar plots

health

across waves

by wave

bids

across waves

by wave

bid x health

across waves

by wave

scatterplots

bids

across waves

by wave

liking

MLM

no brain

bid ~ wave x health

tidytable

plot

model summary

craving

brain ~ health x wave

tidytable

plot

model summary

bid ~ brain x wave

tidy table

plot

by wave

by expression

model summary

bid ~ brain x health x wave

tidy table

plot

by wave

by expression

model summary

brain ~ liking x health

tidy table

plot

model summary

Koban craving

brain ~ health x wave

tidytable

plot

model summary

bid ~ brain x wave

tidy table

plot

by wave